We keep hearing about predictive analysis, deep learning, cognitive computing, machine learning etc. and thanks to Stephen Spielberg, most of us are familiar with the term artificial intelligence (AI). What is so important about these concepts and how can they affect our professional (or personal) lives? Given the pace of change and the potential for disruption, these technologies are definitely something to watch out for.

At one time, we literally needed a screwdriver to program a computer-it was used to install a new vacuum tube or remove a faulty one, and pass electricity in the correct sequence to signal a 0 or 1. Though we have come a long way since then, IT programmers have continued to operate with a similar approach. Instructions are provided to a computer only when the human knows the solution to a problem prior to coding. Advances in techniques such as artificial intelligence, machine learning, and so on are challenging this convention, and knowing the solution or answer in advance is no longer mandatory.

What is Machine Learning?

Machine learning is a crucial component of AI. It allows machines to learn from data, and mimic human responses. There are many who have tried to outline what is machine learning, and the most common definition is the extraction of knowledge from data to make predictions that are statistically justified. Machine learning is all about drawing conclusions, and making predictions about future data instances.

Machine learning started to make its way into the industry in the early 90s, and it started with relatively simple tasks-with assessing credit risk via applications, sorting mail by reading hand-written characters and zip codes, and similar basic functions.

Over the past few years, however, we have made remarkable progress.

Machine Learning is now capable of handling far more complex tasks. A case in point was a research experiment where an algorithm was created that could grade an essay. The result of this algorithm was at par with the grades given by human teachers. Another example was an algorithm that can detect the symptoms of a disease and recommend diagnosis-again this algorithm was able to match the diagnosis given by doctors.

Researchers at Oxford conducted a study on machine learning, according to which 1 in every 2 jobs has the potential of being automated. Given the right data, no doubt that machines are going to outperform humans. A teacher might read 10,000 essays in his or her lifetime; an ophthalmologist may diagnose 30,000 patients. But a machine can read millions of essays and eyes in the span of a minute. When it comes to high volume tasks, we have no chance of competing with the benefits of machine learning.

But there are few areas where machines still lag behind, such as handling entirely unfamiliar situations. They cannot handle what they have not seen several times before. The fundamental limitation of machine learning is that it learns from large volumes of past data interactions. Humans work differently-we have the ability to seamlessly connect distinct threads to solve problems we have never encountered before. Can you imagine machines inventing the microwave oven, simply by associating radar technology with melting of the chocolate?

In my next few posts I will address the ways in which machine learning affects business processes, especially in the Financial Services industry.